convert avgpool and softmax layers, support classification mode in demo

The conversion script supports the global average pooling (avgpool) and
softmax layers required to convert the darknet and tiny networks.
In the demo the --coco option has been replaced by the more generic
'mode'; the 'darknet' mode implements classification on the 1k Imagenet
classes.

create_yolo_prototxt.py

@@ -130,6 +130,12 @@ def max_pooling_layer(previous, name, params):
         kernel_size=int(params["size"]), stride=int(params["stride"]))
 
 
+def global_pooling_layer(previous, name, mode="avg"):
+    """ create a Global Pooling Layer """
+    pool = cp.Pooling.AVE if mode == "avg" else cp.Pooling.MAX
+    return cl.Pooling(previous, name=name, pool=pool, global_pooling=True)
+
+
 def dense_layer(previous, name, params, train=False):
     """ create a densse layer """
     fields = dict(num_output=int(params["output"]))
@@ -191,6 +197,10 @@ def convert_configuration(config, train=False, loc_layer=False):
         elif section == "maxpool":
             layers.append(max_pooling_layer(layers[-1], "pool{0}".format(count),
                                             params))
+        elif section == "avgpool":
+            layers.append(global_pooling_layer(layers[-1], "pool{0}".format(count)))
+        elif section == "softmax":
+            layers.append(cl.Softmax(layers[-1], name="softmax{0}".format(count)))
         elif section == "connected":
             count += 1
             add_dense_layer(layers, count, params, train)
@@ -207,7 +217,7 @@ def convert_configuration(config, train=False, loc_layer=False):
     model = caffe.NetSpec()
     for layer in layers:
         setattr(model, layer.fn.params["name"], layer)
-    model.result = layers[-1]
+    model.result = layers[-1]       
 
     return model